Agents in Healthcare

• SlideDeck: 2026-SP-W3.2-Team05-Agent-Healthcare.pdf
• Version: current
• Notes: HCLS agents

Application

In this session, our readings cover:

Required Readings: AGENT APPLICATIONS

Core Component: Translating Agent Architectures into Real-World Systems

Focus on how agent capabilities are adapted to specific domains and product workflows, including user experience, operational constraints, and measurable impact.

Key Concepts: Domain adaptation, workflow integration, human-in-the-loop design, reliability in production, evaluation in context, compliance and governance, and case studies (software, education, healthcare, finance, science, and robotics)

2025 HIGH-IMPACT PAPERS on a related topic:

• a. Deep Research: A Survey of Autonomous Research Agents (August 2025)
  • Link: https://arxiv.org/html/2508.12752v1
  • Research Agent Architecture:
    • Planning strategies: World model simulation, modular design search, human-like reasoning synthesis, self-refinement
    • World models: LLMs as implicit world models, graph-based structured knowledge
    • Meta-learning: MPO (Meta-Plan Optimization) - adaptive tuning across environments
    • Architecture search: AgentSquare for automatic pipeline assembly
  • DeepResearchBench: Evaluates report fidelity, citation accuracy, comprehensive coverage
  • Key Challenge: Plan brittleness, lack of robustness to ambiguous queries, evaluation coarseness
• b. A Survey of LLM-based Agents in Medicine: How far are we from Baymax?
  • https://arxiv.org/abs/2502.11211
  • Wenxuan Wang, Zizhan Ma, Zheng Wang, Chenghan Wu, Jiaming Ji, Wenting Chen, Xiang Li, Yixuan Yuan
  • Large Language Models (LLMs) are transforming healthcare through the development of LLM-based agents that can understand, reason about, and assist with medical tasks. This survey provides a comprehensive review of LLM-based agents in medicine, examining their architectures, applications, and challenges. We analyze the key components of medical agent systems, including system profiles, clinical planning mechanisms, medical reasoning frameworks, and external capacity enhancement. The survey covers major application scenarios such as clinical decision support, medical documentation, training simulations, and healthcare service optimization. We discuss evaluation frameworks and metrics used to assess these agents’ performance in healthcare settings. While LLM-based agents show promise in enhancing healthcare delivery, several challenges remain, including hallucination management, multimodal integration, implementation barriers, and ethical considerations. The survey concludes by highlighting future research directions, including advances in medical reasoning inspired by recent developments in LLM architectures, integration with physical systems, and improvements in training simulations. This work provides researchers and practitioners with a structured overview of the current state and future prospects of LLM-based agents in medicine.

More readings:

• d. CitySim: Modeling Urban Behaviors with LLM-Driven Agents (2025)
  • Urban simulation using recursive value-driven approach
  • Scalable agent-based modeling for city dynamics
• f. A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools (2025)
  • Referenced in: https://github.com/luo-junyu/Awesome-Agent-Papers
  • Comprehensive taxonomy of FMs in materials science
  • Reviews advances, resources, and future directions
  • Integration of agents in materials discovery workflows
• f. LitMOF: LLM-Driven Multi-Agent Curation of Materials Database (December 2025)
  • Link: https://arxiv.org/abs/2512.01693
  • Problem: Nearly half of Metal-Organic Framework (MOF) database entries contain structural errors
  • Solution: Multi-agent framework validating crystallographic information from literature
  • Results:
    • Curated LitMOF-DB: 118,464 computation-ready structures
    • Corrected 69% (6,161 MOFs) of invalid entries in CoRE MOF database
    • Discovered 12,646 experimentally reported MOFs absent from existing resources
  • Paradigm: Self-correcting scientific databases through LLM-driven curation